《AI大模型应用》-CVPR 2023第一届大模型比赛Track1第1名方案，队名：huster.zip

<div align="center"> <img src="resources/mmdet-logo.png" width="600"/> <div>&nbsp;</div> <div align="center"> <b><font size="5">OpenMMLab website</font></b> ^{<a href="https://openmmlab.com"> <i><font size="4">HOT</font></i> </a>} &nbsp;&nbsp;&nbsp;&nbsp; <b><font size="5">OpenMMLab platform</font></b> ^{<a href="https://platform.openmmlab.com"> <i><font size="4">TRY IT OUT</font></i> </a>} </div> <div>&nbsp;</div> [](https://pypi.org/project/mmdet) [](https://mmdetection.readthedocs.io/en/latest/) [](https://github.com/open-mmlab/mmdetection/actions) [](https://codecov.io/gh/open-mmlab/mmdetection) [](https://github.com/open-mmlab/mmdetection/blob/main/LICENSE) [](https://github.com/open-mmlab/mmdetection/issues) [](https://github.com/open-mmlab/mmdetection/issues) [📘Documentation](https://mmdetection.readthedocs.io/en/latest/) | [🛠️Installation](https://mmdetection.readthedocs.io/en/latest/get_started.html) | [👀Model Zoo](https://mmdetection.readthedocs.io/en/latest/model_zoo.html) | [🆕Update News](https://mmdetection.readthedocs.io/en/latest/notes/changelog.html) | [🚀Ongoing Projects](https://github.com/open-mmlab/mmdetection/projects) | [🤔Reporting Issues](https://github.com/open-mmlab/mmdetection/issues/new/choose) </div> <div align="center"> English | [简体中文](README_zh-CN.md) </div> <div align="center"> <a href="https://openmmlab.medium.com/" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <a href="https://discord.com/channels/1037617289144569886/1046608014234370059" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <a href="https://twitter.com/OpenMMLab" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <a href="https://www.youtube.com/openmmlab" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <a href="https://space.bilibili.com/1293512903" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a> </div> ## Introduction MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the [OpenMMLab](https://openmmlab.com/) project. The main branch works with **PyTorch 1.6+**. <img src="https://user-images.githubusercontent.com/12907710/187674113-2074d658-f2fb-42d1-ac15-9c4a695e64d7.png"/> <details open> <summary>Major features</summary> - **Modular Design** We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules. - **Support of multiple tasks out of box** The toolbox directly supports multiple detection tasks such as **object detection**, **instance segmentation**, **panoptic segmentation**, and **semi-supervised object detection**. - **High efficiency** All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and [SimpleDet](https://github.com/TuSimple/simpledet). - **State of the art** The toolbox stems from the codebase developed by the *MMDet* team, who won [COCO Detection Challenge](http://cocodataset.org/#detection-leaderboard) in 2018, and we keep pushing it forward. The newly released [RTMDet](configs/rtmdet) also obtains new state-of-the-art results on real-time instance segmentation and rotated object detection tasks and the best parameter-accuracy trade-off on object detection. </details> Apart from MMDetection, we also released [MMEngine](https://github.com/open-mmlab/mmengine) for model training and [MMCV](https://github.com/open-mmlab/mmcv) for computer vision research, which are heavily depended on by this toolbox. ## What's New ### Highlight We are excited to announce our latest work on real-time object recognition tasks, **RTMDet**, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in the [technical report](https://arxiv.org/abs/2212.07784). Pre-trained models are [here](configs/rtmdet). [](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real) [](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real) [](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real) | Task | Dataset | AP | FPS(TRT FP16 BS1 3090) | | ------------------------ | ------- | ------------------------------------ | ---------------------- | | Object Detection | COCO | 52.8 | 322 | | Instance Segmentation | COCO | 44.6 | 188 | | Rotated Object Detection | DOTA | 78.9(single-scale)/81.3(multi-scale) | 121 | <div align=center> <img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/> </div> **v3.0.0** was released in 6/4/2023: - Release MMDetection 3.0.0 official version - Support S